Faculty of Computing

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Author: search.filters.author.Jayakody, AStart date: 2025

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Now showing 1 - 3 of 3
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    PublicationOpen Access
    Enhancing blockchain technology adoption in governmental operations: A comprehensive framework for user adoption
    (PloS one, 2026-07-06) Gnanasekara, A; Jayakody, A; Perera, K
    The purpose of this study is to investigate the factors that affect Blockchain adoption in governmental operations in Sri Lanka and to propose a comprehensive adoption framework for Blockchain technology in the Sri Lankan governmental operations. The Technology- Organization-Environment (TOE) framework is utilized due to its capacity to capture the complexities of technological adoption in the public sector, addressing both internal (organizational) and external (environmental) factors that influence the adoption process. Given the structural, regulatory, and data sensitivity challenges of governmental settings, the TOE framework integrates employee insights from technological, organizational, and environmental perspectives, making it adaptable to the public sector's needs and scalable across various government entities. It also reflects the regulatory and operational requirements specific to the Sri Lankan public institutions, including essential compliance areas such as data privacy, security regulations, and government workflows, thereby offering a practical pathway for Blockchain adoption within the local context. This study employed statistical methods to ensure the validity and reliability of data collected through a structured questionnaire distributed to Grade I-IT Directors to capture their perceptions and experiences with Blockchain technology. Using the structural equation modelling (SEM), the study finds that all the technological, organizational, and environmental dimensions of the TOE framework are significantly associated with intention to adopt Blockchain technology in the Sri Lankan governmental operations. At a more specific level, trust, compatibility, security, higher authority support, monetary resources, rivalry pressure, and regulatory support were identified as significant predictors of adoption intention, while relative advantage, IT resources, and business partner pressure were not statistically significant, and firm size showed only weak support. These findings provide an empirically grounded framework for understanding Blockchain adoption intention in the Sri Lankan public sector and offer implications for future policy and implementation planning.
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    PublicationOpen Access
    A novel application with explainable machine learning (SHAP and LIME) to predict soil N, P, and K nutrient content in cabbage cultivation
    (Elsevier B.V., 2025-03-06) Abekoon, T; Sajindra, H; Rathnayake, N; Ekanayake, I, U; Jayakody, A; Rathnayake, U
    Cabbage (Brassica oleracea var. capitata) is commonly cultivated in high altitudes and features dense, tightly packed leaves. The Green Coronet variety is well-known for its robust growth and culinary versatility. Maximizing yield is crucial for food sustainability. It is essential to predict the soil’s major nutrients (nitrogen, phosphorus, and potassium) to maximize the yield. Artificial intelligence is widely used for non-linear predictions with explainability. This research assessed the predictive capabilities of soil nitrogen, phosphorus, and potassium levels with explainable machine learning methods over an 85-day cabbage growth period. Experiments were conducted on cabbage plants grown in central hills of Sri Lanka. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were used to clarify the model’s predictions. SHAP analysis showed that high feature values of the number of days and plant average leaf area negatively impacted for nutrient predictions, while high feature values of leaf count and plant height had a positive effect on the nutrient predictions. To validate the results, 15 greenhouse-grown cabbage plants at various growth stages were selected. The nitrogen, phosphorus, and potassium levels were measured and compared with the predicted values. These insights help refine predictive models and optimize agricultural practices. A user-friendly application was developed to improve the accessibility and interpretation of predictions. This tool is a user-friendly platform for end-users, enabling effective use of the model’s predictive capabilities.
  • Thumbnail Image
    PublicationOpen Access
    A novel application with explainable machine learning (SHAP and LIME) to predict soil N, P, and K nutrient content in cabbage cultivation
    (Elsevier B.V., 2025-08) Abekoon, T; Sajindra, H; Rathnayake, N; Ekanayake, I.U.; Jayakody, A; Rathnayake, U
    Cabbage (Brassica oleracea var. capitata) is commonly cultivated in high altitudes and features dense, tightly packed leaves. The Green Coronet variety is well-known for its robust growth and culinary versatility. Maximizing yield is crucial for food sustainability. It is essential to predict the soil's major nutrients (nitrogen, phosphorus, and potassium) to maximize the yield. Artificial intelligence is widely used for non-linear predictions with explainability. This research assessed the predictive capabilities of soil nitrogen, phosphorus, and potassium levels with explainable machine learning methods over an 85-day cabbage growth period. Experiments were conducted on cabbage plants grown in central hills of Sri Lanka. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were used to clarify the model's predictions. SHAP analysis showed that high feature values of the number of days and plant average leaf area negatively impacted for nutrient predictions, while high feature values of leaf count and plant height had a positive effect on the nutrient predictions. To validate the results, 15 greenhouse-grown cabbage plants at various growth stages were selected. The nitrogen, phosphorus, and potassium levels were measured and compared with the predicted values. These insights help refine predictive models and optimize agricultural practices. A user-friendly application was developed to improve the accessibility and interpretation of predictions. This tool is a user-friendly platform for end-users, enabling effective use of the model's predictive capabilities.